AnnoGram4MD: A Language for Annotating Grammars for High Quality Metamodel Derivation

Abstract

The quests for transfers of software artifacts between the model ware and grammar ware technical spaces have increased in recent decades. Particularly, the need to port grammar-based concepts into the model ware space has birthed efforts to synthesise Ecore-based metamodels from Extended Backus Naur Form (EBNF)-based grammars. However, automatic derivation of high quality metamodels from grammars is still a challenge as existing solutions produce metamodels containing either superfluous classes or anonymous classifiers or both, making the results less useful. AnnoGram4MD addresses these issues by adding special annotations to the grammar as complementary information to guide the derivation algorithm towards producing high-quality metamodels. A comparison of AnnoGram4MD with existing solutions when applied to a sample grammar reduced the number of EClassifiers by 52% and without anonymous EClassifiers in the generated metamodel.